Machine Learning Training Bootcamp

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Information about Machine Learning Training Bootcamp

Published on July 21, 2018

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Slide 1: Price:  $3,999.00 Length:  3 Days AI Artificial intelligence  RS Recommender systems IoT Internet of Things Call Tonex Experts Today: +1-972-665-9786 MACHINE LEARNING TRAINING BOOTCAMP Machine Learning Techniques Logistic regression K-nearest neighbors Naive Bayes Basic Python Decision tree regression VISIT TONEX.COM Slide 2: Machine Learning Training Bootcamp Machine Learning training bootcamp is a 3-day specialized training course that covers the essentials of machine learning, a shape and utilization of man-made reasoning (AI). Machine learning computerizes the information investigation process by empowering PCs, machines and IoT to learn and adjust through experience connected to particular undertakings without unequivocal programming. Participants will learn, appreciate and ace thoughts on machine learning ideas, key standards, and methods including managed and unsupervised learning, scientific and heuristic angles, displaying to create calculations, forecast, direct relapse, bunching, grouping, and expectation. Learn contrasts and likenesses between Machine Learning, Artificial Intelligence and Data Mining. Man-made brainpower utilizes models worked by Machine Learning to make savvy conduct connected to organizations, showcasing and deals, activities, self-sufficient autos, recreations and modern robotization by predicion in view of guidelines and utilizing programming dialects and calculations. Slide 3: Machine Learning Training Bootcamp Machine learning based on artificial intelligence provides the ability to learn about newer data sets without being programmed explicitly using methods of data analysis. Machine Learning takes advantages of Data Mining techniques, statistics, other key principles and learning algorithms to build models to predict future outcomes. Math and programming are the basis for many of the machine learning algorithms. Using machine learning as a tool, the machine must automatically learn the parameters of models from the data. Using larger datasets, better accuracy and performance is achieved.  Slide 4: Machine Learning Training Bootcamp Machine learning and data mining can use the same key algorithms to discover patterns in your data and dataset. In machine learning, the computers, machines and IoT devices must automatically learn the parameters of models from the data using self-learning algorithms to reveal insights and provide feedback in near real-time. Machine learning, for example, can be used in proactive maintenance to continuously monitor the performance of simple or complex industrial systems, applications and events. Using the ability to learn and adapt, makes it the optimal choice for improvements in ongoing processes, and to automatically predict and prevent failures.  Learn how Machine Learning can automatically process and analyze huge volumes of complex data. Machine learning powers innovative automated technologies such as recommendation engines, facial recognition, financial losses from stock market and bonds, fraud protection, self-driving autonomous cars, robotics, industrial automation and future applications. Slide 5: Machine Learning Training Bootcamp Learning Objectives Learn about Artificial Intelligence and Machine Learning List similarities and differences between AI, Machine Learning and Data Mining Learn how Artificial Intelligence uses data to offer solutions to existing problems Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize / Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns List the various applications of machine learning and related algorithms Learn how to classify the types of learning such as supervised and unsupervised learning Implement supervised learning techniques such as linear and logistic regression Use unsupervised learning algorithms including deep learning, clustering and recommender systems (RS) used to help users find new items or services, such as books, music, transportation, people and jobs based on information about the user or the recommended item Learn about classification data and Machine Learning models Select the best algorithms applied to Machine Learning Make accurate predictions and analysis to effectively solve potential problems List Machine Learning concepts, principles, algorithms, tools and applications Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning / Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering and recommendation systems Slide 6: Machine Learning Training Bootcamp Course Agenda and Topics The Basics of Machine Learning Machine Learning Techniques, Tools and Algorithms Data and Data Science Review of Terminology and Principles Applied Artificial Intelligence (AI) and Machine Learning Popular Machine Learning Methods Learning Applied to Machine Learning Principal component Analysis Principles of Supervised Machine Learning Algorithms Principles of Unsupervised Machine Learning Regression Applied to Machines Learning Principles of Neural Networks Large Scale Machine Learning Introduction to Deep Learning Applying Machine Learning Overview of Algorithms Overview of Tools and Processes Slide 7: Machine Learning Training Bootcamp The Basics of Machine Learning What is Machine Learning? Emergence and applications of Artificial Intelligence and Machine Learning Basics of Artificial Intelligence Basics of Machine Learning Basics of Data Mining Data Mining versus Machine Learning versus Data Science Data Mining and patterns Why is machine learning important? Creating good machine learning systems Slide 8: Machine Learning Training Bootcamp Machine Learning Techniques, Tools and Algorithms Supervised, unsupervised, semi supervised and reinforcement learning Basic tools and ideas in Machine Learning Supervised Machine Learning problems and solutions Supervised Machine Learning tasks subgroups: regression and classification Unsupervised Machine Learning Unsupervised tasks and generative modelling Reinforcement Learning, Hybrids and Beyond Data preparation capabilities Techniques of Machine Learning Polynomial regression Linear regression Random forest Decision tree regression Gradient descent and regularization Classification Logistic regression K-nearest neighbors Support vector machines Naive Bayes Kernel support vector machines Decision tree classifier Random forest classifier Clustering algorithms K-means clustering Bias and variance trade-off Representation learning Data Preprocessing Data preparation Feature engineering and scaling Data and Datasets Dimensionality reduction Slide 9: Machine Learning Training Bootcamp Random forest classifier Clustering algorithms K-means clustering Bias and variance trade-off Representation learning Data Preprocessing Data preparation Feature engineering and scaling Data and Datasets Dimensionality reduction Data and Data Science Principles of Data science Programming, logical reasoning Mathematics and statistics Data Engineering versus Data Science Time series comparison Neural Networks Steps to Machine Learning Slide 10: Machine Learning Training Bootcamp Review of Terminology and Principles Math Refresher Concepts of linear algebra Probability and statistics Algorithms Automation and iterative processes Scalability Ensemble modeling Framing Generalization Machine Learning methods Classification Training and Training Set Validation Representation Regularization Logistic Regressions Neutral Nets Neutral Nets Multi class Neutral Nets Embeddings Basic Algebra and Calculus Basic Python Chain rule Concept of a derivative Gradient or slope Linear algebra Logarithms, and logarithmic equations   Matrix multiplication Mean, median, outliers and standard deviation Partial derivatives Sigmoid function Statistics Tanh Tensor and tensor rank Trigonometry Variables, coefficients, and functions Slide 11: Machine Learning Training Bootcamp Applied Artificial Intelligence (AI) and Machine Learning Machine Learning prediction with models Artificial Intelligence behaving and reasoning Applications of Machine Learning Machine Learning algorithms Models Techniques Statistics and Math Algorithms Programming Patterns and Prediction Intelligent Behavior Statistics quantifies numbers Machine learning generalizing information from large data sets Principles to detect and extrapolate patterns Machine Learning System Analysis and Design Support Vector Machines Slide 12: Machine Learning Training Bootcamp Popular Machine Learning Methods Supervised learning and unsupervised learning Supervised learning algorithms and labeled data Trained using labeled examples Classification, regression, prediction and gradient boosting Supervised learning and patterns Predicting the values of the label on additional unlabeled data Using historical data to predict likely future events Unsupervised learning and unlabeled data Unsupervised learning against data that has no historical labels Semi supervised learning Using both labeled and unlabeled data for training Classification, regression and prediction Reinforcement learning Robotics, gaming and navigation Discovery through trial and error The agent (the learner or decision maker) The environment (everything the agent interacts with) Actions (what the agent can do) Slide 13: Machine Learning Training Bootcamp Learning Applied to Machine Learning Application of Supervised versus Unsupervised Learning Case Study: credit card transactions as fraudulent charges Self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition Face recognition Principal component Analysis Anomaly detection Deep learning Neural networks Learning with deep neural networks Deep neural networks and hidden layers and multiple types of hierarchies Deep learning as a type of machine learning Regularization Machine learning models need to generalize well to new examples that the model has not seen in practice. Tools to prevent models from overfitting the training data. Slide 14: Machine Learning Training Bootcamp Principles of Supervised Machine Learning Algorithms Machine Learning algorithms mind map What is supervised machine learning? How does it relate to unsupervised machine learning? Classification and regression supervised learning problems Clustering and association unsupervised learning problems Algorithms used for supervised and unsupervised problems Supervised Machine Learning as a majority of practical machine learning Supervised learning problems grouping into regression and classification problems Principles of “Classification” Principles of “Regression” Popular examples of supervised machine learning algorithms Linear regression for regression problems Random forest for classification and regression problems Support vector machines for classification problems Slide 15: Machine Learning Training Bootcamp Principles of Unsupervised Machine Learning The goal for unsupervised learning Modeling the underlying structure or distribution in the data Ways to learn more about the data Algorithms to discover and present the interesting structure in the data Unsupervised learning problems grouping into clustering and association problems Principles of “Clustering” Ways to discover the inherent groupings in the data Principles of “Association” Ways to discover rules that describe large portions of your data Examples of unsupervised learning algorithms K-means for clustering problems Apriori algorithm for association rule learning problems Semi-Supervised Machine Learning Unlabeled data and a mixture of supervised and unsupervised techniques Collecting and storing unlabeled data Slide 16: Machine Learning Training Bootcamp Regression Applied to Machines Learning Linear Regression with One Variable Application of linear regression Method for learning Linear Algebra Review Refresher on linear algebra concepts Models with multiple variables Linear Regression with Multiple Variables Implement the learning algorithms in practice Logistic Regression Logistic regression is a method for classifying data into discrete outcomes Logistic regression to classify a credit card transaction as fraud or not fraud Slide 17: Machine Learning Training Bootcamp Principles of Neural Networks Neural Networks Representation Principles behind neural networks and models Neural Networks Learning Back-propagation algorithm Learn parameters for a neural network. Implementing your own neural network for credit card fraud Advice for Applying Machine Learning Best practices for applying machine learning in practice Best ways to evaluate performance of the learned models Slide 18: Machine Learning Training Bootcamp Large Scale Machine Learning Real-world case studies Interactive visualizations of algorithms in action Pattern Recognition Accuracy Case Study: Marketing Campaign Working with Regression Prediction Classification Logistic Regression Unsupervised Learning with Clustering Slide 19: Machine Learning Training Bootcamp Introduction to Deep Learning Principles of Deep Learning Artificial Neural Networks TensorFlow Learning complicated patterns in large amounts of data Identifying objects in images and words in sounds Automatic language translation Medical diagnoses Slide 20: Machine Learning Training Bootcamp Applying Machine Learning Applying machine learning to IoT Machine learning can be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things. Financial services DoD Government Health care Marketing and sales Oil and gas Renewable Energy Transportation Slide 21: Machine Learning Training Bootcamp Overview of Algorithms Associations and sequence discovery Bayesian networks Decision trees Expectation maximization Gaussian mixture models Gradient boosting and bagging Kernel density estimation K-means clustering Local search optimization techniques Multivariate adaptive regression splines Nearest-neighbor mapping Neural networks Principal component analysis Random forests Self-organizing maps Sequential covering rule building Singular value decomposition Support vector machines Slide 22: Machine Learning Training Bootcamp Overview of Tools and Processes Comprehensive data quality and management GUIs for building models and process flows Interactive data exploration Visualization of model results Comparisons of different machine learning models Identify the best machine learning models Automated ensemble model evaluation Repeatable and reliable results Integrated, end-to-end platforms to automate data-to-decision process Exploratory Data Analysis with R Loading, querying and manipulating data in R Cleaning raw data for modeling Reducing dimensions with Principal Component Analysis Identifying outliers in data Working with Unstructured Data Mining unstructured data Building and evaluating association rules Constructing recommendation engines Machine learning with neural networks Slide 23: Call Tonex Experts Today: +1-972-665-9786 MACHINE LEARNING TRAINING BOOTCAMP VISIT TONEX.COM Slide 24:

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